As a subset of artificial intelligence, computers’ ability to reason, machine learning implies algorithms that learn through training data instead of programming. Specifically, the purpose of machine learning is to add business value via predictions and resolutions by giving computers the ability to learn using data. Since algorithms and data structures are the foundations of computer programming, do you need to know these for machine learning?
You need to know data structures for machine learning as they are the building blocks of computer programming. This is necessary to develop a deep understanding and gain machine learning expertise. Understanding data structures also gives you an advantage over other machine learning professionals.
While it has been determined that data structures are foundational for machine learning, this article more deeply defines both concepts and discusses the correlation between the two. Also, keep reading to review qualifications and careers for machine learning practitioners.
Important Sidenote: We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and identified 6 proven steps to follow for becoming a data scientist. Read my article: ‘6 Proven Steps To Becoming a Data Scientist [Complete Guide] for in-depth findings and recommendations! – This is perhaps the most comprehensive article on the subject you will find on the internet!
Table of Contents
Overview of Machine Learning and Data Structures
Artificial intelligence is a concept in which the computer solves a task, for instance, using a virtual assistant or a self-driven car instead of a human performing these tasks. Machine learning is a subset of artificial intelligence. It is applied to solve human problems by utilizing algorithms and data structure through statistical methods to learn by example instead of being programmed.
Data, units of information, are collected, analyzed, and reported. And data structures are the physical representation of that data, organization of data, and are the basis for the abstract data type (ADT).
As a theory, ADT looks at the possible behavior of data and is used for designing data structures and algorithms. Whereas data structure is a real collection of values, with concrete relationships to each other and defined by which operations can be implemented to that data.
Correlation Between Data Structures and Machine Learning
If using machine learning to resolve a problem, you need to evaluate which model is fastest and consumes the smallest amount of space and resources but accurately solves it. If a model is built by utilizing algorithms, comparing and contrasting two algorithms to determine the best for the job is crucial to the machine learning professional. Therefore, mastery of data structures and algorithms is a necessary part of the job.
Specifically, when you know data structures and algorithms, you can answer the following questions:
- How much memory is required to execute?
- How long will it take to run?
- With the business case on hand, which algorithm will offer the best performance?
There are varying degrees of knowledge required as a machine learning practitioner. If you are in research, the emphasis is on understanding data and building models based on that understanding. If you are a machine learning practitioner in production, a larger grasp of data structures, algorithms, and computer systems is necessary to drive business solutions to productionalization.
What Are Data Structures?
As a broad overview, data structures are groupings of data, algorithms use the data as input, and provide step by step directions to carry out a task. Programming languages interpret how you want the directions for the task carried out so that the computer understands.
Although data structures are ordered sequences of data and are the same for all computer programming languages, they are implemented differently. Starting from the beginning, a data type tells the compiler how the programmer will be using the data, for instance, as a character or an integer. Specifically, the data type is an attribute used by computer programming languages to recognize how the programmer plans to use the data.
Primitive data types:
- Boolean values
- Characters
- Fixed point numbers
- Floating point numbers
- Integers
Composite data types:
- Arrays
- Records
- Unions
Abstract data types:
- Lists
- Sets
- Tuples
Built with data types, there are quite a few different data structures available. In particular, data structures are the organization, management, and storage of data; however, the creation and manipulation of that data structure involve writing a set of procedures. Common data structures with brief explanations are categorized as seen here:
- Linear data structures:
- Arrays are elements in a specific order and usually of the same type, such as the lookup table.
- Lists are collections of data elements of any type, called nodes, which have a value and point to the next node in the list. A linked list can change the details of a list and is mutable.
- Tuples can’t change the elements of a list and are immutable.
- Sets are built as a sequence of unique items or identifiers.
- Tree data structures:
- Binary tree is a tree with two children.
- Hash-based structures:
- Hash table distributes the entries across an array.
- Graph data structures:
- Graphs are directed or undirected concepts with nodes and ordered or unordered pairs.
A computer programmer should be aware of some basic algorithms and when and where to use them. The properties of an algorithm are that input and output are specified; it is defined, effective, and limited. For example:
- Sort algorithm allows you to arrange data in a particular order: counting sort, merge sort, and Quicksort.
- Search algorithms, for instance, searching through a list in which string matching occurs. An example is a bot in artificial intelligence.
- Hash lookup is a combination of sorting and searching in which data is looked up using a key, as kept in a hash table.
- Dynamic programming is solving a complex issue by breaking it into elemental issues, resolving each one separately, and using those solutions toward resolving the bigger issue.
- Exponentiation by squaring is a mathematical calculation used for faster computations.
- Primality testing algorithm determines whether or not a number is prime.
- String matching the issue of pattern and string matching, for example, the Knuth-Morris-Pratt algorithm.
Mathematics and Statistics
Along with data structures and algorithms, you need to learn mathematics, statistics, and probability to make sense of the data, recognize patterns, and create insights for business purposes relating to the data outcomes. Statistics is used to create insightful data models and is necessary to resolve business problems.
To thoroughly understand the business issues, a model was made to predict how likely something will occur. If it is likely, the machine learning data scientist will use the data pipeline, execute an algorithm to train the data, and build a prediction model to derive an outcome, using different types of machine learning as broadly defined here. These types can be mixed and matched in any way that makes sense to the machine learning professional.
- The metaphor, learning from the teacher, or supervised learning happens when an algorithm is executed to predict business outcomes based on training data input.
- Another metaphor, learning with no teacher feedback, or unsupervised learning, happens when the outcome is determined based on probabilities based on data patterns and associations.
- With reinforcement learning, observations are made, and actions are performed. As rewards or penalties are given out, the correct path is determined.
- Batch learning is taken offline because the system cannot learn incrementally from an oncoming stream of data.
- Online learning works well when a continuous stream of data is incoming. The system learns incrementally on the fly as it’s being fed the data.
Computer Programming
Learn simple programming before implementing algorithms. A user-friendly programming language like Python is used to write algorithms to execute data. Instead of reinventing the wheel, you can find huge libraries of already tried and tested algorithms, as well as community support. Although you may not be required to write to them, you will want to understand enough to read, execute, and customize applicable algorithms.
Python is general-purpose and easy to learn the language, integrates well with other languages, has an extensive library and community support, and works on any platform. An extensive resource is the Python official home for all things about the language. And of great use are query languages, such as SQL, a programming language tool used to manipulate data and work well with Python.
For Python, free software, scikit learn, or sklearn was developed and first released in 2010. As a machine learning library extensively used in the artificial intelligence industry, it has increased in popularity.
It is designed to work interconnecting with Python’s numerical libraries, which is important in machine learning. Some of the common algorithms included are classification, clustering, and regression. Another resource, IPython, is a virtual notebook for interactive data analysis and modeling that can be run from a browser and is shareable.
Using algorithm libraries is efficient if you know how to choose the applicable computer instructions to grant the necessary results as a computer programmer. The data structure allows you to organize data and compare algorithms, but machine learning is a programming language used to write and execute algorithms.
Do you want to learn more? A course for Machine Learning uses Python programming language to explain core ideas in machine learning and artificial intelligence using over 15 case studies. Producible solutions to real-world business issues are worked out in this online video course of over 150 hours of content.
As the preferred data science and machine learning language, there are many certificates available online. The Python Certificate Training for Data Science includes a combination of the essentials of Python and related statistics. It provides practice with over 42 hours of instruction with many machine learning types, such as supervised and unsupervised learning.
For a sense of community, the Python website includes a community section that is mighty and diverse. The forums are active, and it’s easy to form or join a particularly interesting group or attend a conference. Keep in touch via a weekly email, Internet Relay Chat, or if you use Slack, become a PySlacker. The libraries are vast on the website or join GitHub for Python.
What Is Machine Learning?
Artificial intelligence is a broad concept in which computer systems demonstrate tasks typically performed by humans, such as facial recognition. As a subset of artificial intelligence, machine learning collects insights from large data sets and automatically learns to predict and drive business value. More specifically, algorithms are built utilizing data structures (like arrays) to execute using training data and subsequently learn from the feedback.
Basic Qualifications
Math concepts, for instance, statistics, probability, and linear algebra are based on machine learning algorithms, which are pieced together as techniques and approaches to develop a specific task. Face recognition or predicting the weather are problems solved by combining applicable algorithms. But to build the algorithms, data structures are necessary, and to execute the algorithms, computer programming languages, such as Python, are used.
Expertise and Deep Understanding
If data structures and algorithms are overly long and of inferior quality, it is considered a bad design, causing slow output and overextension of computing resources. For testing, it may not be relevant, but the inefficiency of design is not useful or accepted for production.
Therefore, the key is to have a deep understanding of the building blocks and understand the relationships of data structures and optimization of solutions. The foundation is knowing how to gather, analyze, and store data and is the underlying support for data science, artificial intelligence, and as a related subset, machine learning.
Advanced algorithms in machine learning require a deep dive into the structure that holds the data. For example, if arrays are understood thoroughly, you can work with NumPy, a library for the computer programming language Python and supports arrays and other math functions.
Only by understanding data structures and algorithms will you determine how NumPy can make array computing better and what is applicable and fits best in the business case you are working on. Otherwise, it becomes an exercise in accuracy, repeating the same code wherever it works, instead of thoughtfully deciding how algorithms should be implemented.
To further define your expertise, as a subset of machine learning, the deep learning concept is an output from the execution of algorithms. Huge sets of data are trained by executing algorithms in the neural network space. In deep learning, neural networks are developed to mimic brain functions to achieve better accuracy with predictions and resolutions for business problems.
Careers and Resources in Machine Learning
Companies post a variety of job titles that utilize machine learning skills. If breaking into the field, carefully read the qualifications, and ask many questions regarding the qualifications expected. If there are holes in your experience, ask about professional development, mentorship within the company, or take classes on your own. Here are a few of the applicable titles:
- Data analyst
- Data analytics developer
- Data analytics engineer
- Data scientist
- Machine learning developer
- Machine learning engineer
- Machine learning scientist
- Statistical programmer
- Statistician
This 14-minute video discusses how to leverage your skills into a machine learning career. Tips for transitioning into the field and opportunities are analyzed by a current machine learning engineer.
A Walmart data scientist describes the technical skills required to develop a machine learning career in this 18-minute video. Pratik Anjay professes that the field of artificial intelligence, of which machine learning is a subset, is expected to increase by 2.3 million jobs worldwide by the end of 2020.
Kirsch Naik, a data scientist, shares how he learned machine learning in 3 months in this 11-minute video. Albeit, he is not a newbie when it comes to programming; therefore, a lengthier training period is inevitable for the beginner.
An excellent resource for learning is Andrew Ng’s Coursera.com class entitled Machine Learning. He is a Stanford University professor and a former Chief Scientist. Overall, with a career as a machine learning practitioner, you will need to add the following to your skillset:
- Mathematics and statistical knowledge.
- Computer programming language.
- Data visualization, both static and dynamic reporting.
- Communicate and report in clear, concise business language to all levels.
To specialize, areas of interest to hone in on are:
- Deep learning architectures.
- Natural language processing is based on the interaction of the human voice and the computer.
- Computer vision utilizing the human vision system and what it can do.
- Bayesian methods are used in complex statistical models.
The book, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, has a 5-star rating on Amazon.com. One section that sets the book apart from others is the section on potential obstructions to selecting the proper algorithm and training it with data. By understanding the problems ahead of time, you can bypass issues that lead to bad results.
Author’s Recommendations: Top Data Science Resources To Consider
Before concluding this article, I wanted to share few top data science resources that I have personally vetted for you. I am confident that you can greatly benefit in your data science journey by considering one or more of these resources.
- DataCamp: If you are a beginner focused towards building the foundational skills in data science, there is no better platform than DataCamp. Under one membership umbrella, DataCamp gives you access to 335+ data science courses. There is absolutely no other platform that comes anywhere close to this. Hence, if building foundational data science skills is your goal: Click Here to Sign Up For DataCamp Today!
- IBM Data Science Professional Certificate: If you are looking for a data science credential that has strong industry recognition but does not involve too heavy of an effort: Click Here To Enroll Into The IBM Data Science Professional Certificate Program Today! (To learn more: Check out my full review of this certificate program here)
- MITx MicroMasters Program in Data Science: If you are at a more advanced stage in your data science journey and looking to take your skills to the next level, there is no Non-Degree program better than MIT MicroMasters. Click Here To Enroll Into The MIT MicroMasters Program Today! (To learn more: Check out my full review of the MIT MicroMasters program here)
- Roadmap To Becoming a Data Scientist: If you have decided to become a data science professional but not fully sure how to get started: read my article – 6 Proven Ways To Becoming a Data Scientist. In this article, I share my findings from interviewing 100+ data science professionals at top companies (including – Google, Meta, Amazon, etc.) and give you a full roadmap to becoming a data scientist.
Conclusion
As with any career, build a solid base and plan on continuing to grow, especially in the field of data science, specifically with the freshness of the up and coming machine learning workspace. In particular, with this specialty, it’s important to love data, mathematics, and computer programming because these concepts are intricately woven.
Understanding data structure and placing the best algorithm into code will give you the ability to forecast correctly and predict solutions in the most optimum way, which is the purpose of machine learning. Moreover, having a strong foundation in data structures and all things data related will help you learn more quickly and become a better version of yourself in the machine learning space.
BEFORE YOU GO: Don’t forget to check out my latest article – 6 Proven Steps To Becoming a Data Scientist [Complete Guide]. We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and created this comprehensive guide to help you land that perfect data science job.
Affiliate Disclosure: We participate in several affiliate programs and may be compensated if you make a purchase using our referral link, at no additional cost to you. You can, however, trust the integrity of our recommendation. Affiliate programs exist even for products that we are not recommending. We only choose to recommend you the products that we actually believe in.
